{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 1.0409 - acc: 0.8000 - val_loss: 1.3903 - val_acc: 0.7860 - time: 1.7692\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 67ms/step - test_loss: 1.6745 - test_acc: 0.8110 - time: 0.0671\n",
      "Test loss 1.6745, Test accuracy 81.10%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import FastGCN\n",
    "model = FastGCN(graph, device='GPU', attr_transform=\"normalize_attr\", seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"fast_gcn\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 32)           45856       attr_matrix[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 32)           0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution (GraphConvolu (None, 7)            224         dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 46,080\n",
      "Trainable params: 46,080\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
